nicklauskim

nicklauskim

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nicklauskim's repositories

nba-shooting-analytics

Data science project exploring the various capabilities of Python libraries such as Numpy, Pandas, Matplotlib, and Seaborn. In the end, I use advanced shooting metrics to analyze the shooting tendencies and similarities of NBA players in the 2019-20 regular season and apply some basic unsupervised learning algorithms to find the most similar players.

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2020-Election-Tweets

Sentiment Analysis Project on Tweets Related to the 2020 Presidential Election

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tabular-synthetic-data-privacy-auditing

Evaluating the privacy of tabular synthetic data generators using an adversarial toolbox, specifically the TAPAS toolbox as introduced in https://arxiv.org/abs/2211.06550. This code is used to produce the results presented in my master's thesis.

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bayesian-mortality-prediction

Bayesian Mortality Prediction with Sensitivity Analysis

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cookie-price-analysis

Time-Series Analysis course project

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Data-Science-Interview-Questions-Answers

Curated list of data science interview questions and answers

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hyperparameter-selection-experimental-design

Hyperparameter Tuning for Gradient Boosting Frameworks

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machine-learning-methods

Various implementations of some common machine learning algorithms in Python, including models from scratch and applied problems such as NLP tasks.

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NLP-Sparse-Matrix-Problem

Dimensionality Reduction in Binary Text Classification Using Regularized Logistic Regression

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gibbs-sampler-multivariate

A Gibbs sampler implementation for a general multivariate normal distribution, written in Python

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hello-world

First repository, from tutorial

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nicklauskim

Config files for my GitHub profile.

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nicklauskim.github.io

My professional website, displaying my projects and other work in statistics and data science.

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old-website

🐟 Nicklaus Kim's professional and academic website using Github Pages and Bay, a simple theme for Jekyll.

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Statistical-Learning-Text-Classification

Machine Learning Methods for Text Classification in Python

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stats202a

Baseball Data Analysis in R and C

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